Prediction of Deleterious Functional Effects of Non-Synonymous Single Nucleotide Polymorphisms in Human Nuclear Receptor Genes Using a Bioinformatics Approach

Abstract

The nuclear receptor (NR) superfamily represents an important group of regulating factors that control the expression of a number of target genes including those encoding important drug metabolizing enzymes and drug transporters. Single nucleotide polymorphism (SNP) is the most common mutation in the human genome and a large number of SNPs have been identified to date. It is unlikely to examine the functional impact of all these mutations using an experimental approach. As such, we employed two algorithms, Sorting Intolerant from Tolerant (SIFT) and Polymorphism Phenotyping (PolyPhen) to predict the impact of non-synonymous SNPs (nsSNPs) on NR activities and disease susceptibility. We identified 442 nsSNPs in a systematic screening of 48 human NR genes. Using SIFT, of 442 amino acid substitutions, 289 (65.38%) were classified as "intolerant". The PolyPhen program classified 269 (60.86%) of them as "probably damaging" or "possibly damaging". The results from the two algorithms were in concordance. Among the 442 mutations, 229 of them have been functionally characterized. SIFT predicted 192 of these nsSNPs as "intolerant", resulting in a correct prediction rate of 83.84%, while PolyPhen gave a prediction rate of 76.86%. For 216 nsSNPs of the androgen receptor gene, 149 nsSNPs have been functionally studied and most (121) of them resulted in a reduction of receptor activity. SIFT sorted 187 out of 216 as "intolerant" (86.57%) and PolyPhen identified 159 out of 216 as "potentially intolerant" (73.61%). These results indicate that both SIFT and PolyPhen are useful and efficient tools to predict the functional effects of nsSNPs of human NR genes.